Non-rigid infrared and visible image registration by enhanced affine transformation

Min, Chaobo; Gu, Yan; Li, Yingjie; Yang, Feng

doi:10.1016/j.patcog.2020.107377

Cited by 27 publications

(11 citation statements)

References 31 publications

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“…Common MMRS images include cross-temporal, cross-season, optical to SAR, optical to infrared, optical to Light Detection and Ranging (LIDAR), map to visible, etc. In the existing literature, research on optical-SAR [12][13][14] and optical-infrared [15][16][17] is most common. We discuss registration methods for MMRS images, rather than the types of modal image pairs.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

et al. 2021

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With rapid advancements in remote sensing image registration algorithms, comprehensive imaging applications are no longer limited to single-modal remote sensing images. Instead, multi-modal remote sensing (MMRS) image registration has become a research focus in recent years. However, considering multi-source, multi-temporal, and multi-spectrum input introduces significant nonlinear radiation differences in MMRS images for which researchers need to develop novel solutions. At present, comprehensive reviews and analyses of MMRS image registration methods are inadequate in related fields. Thus, this paper introduces three theoretical frameworks: namely, area-based, feature-based and deep learning-based methods. We present a brief review of traditional methods and focus on more advanced methods for MMRS image registration proposed in recent years. Our review or comprehensive analysis is intended to provide researchers in related fields with advanced understanding to achieve further breakthroughs and innovations.

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Section: Introductionmentioning

confidence: 99%

Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

et al. 2021

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“…Affine transformation [ 9 ] is commonly used because it can maintain the fixed linear state and parallel relation in the image before and after transformation. The affine transformation model includes four types of image transformations: translation, rotation, scaling, and shearing.…”

Section: Methodsmentioning

confidence: 99%

“…Image registration is an essential step to ensure fusion operation, which aligns two or more images from different times, sensors, and views by finding a credible spatial transformation [ 8 ]. However, due to the complementary information and different imaging principles of multi-sensor images, the mutual information of infrared and visible images is less [ 9 ]. It is a challenge to find correspondences for infrared-visible image registration [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Infrared-Visible Image Registration Method Based on the Constrained Point Feature

Han

Liu

et al. 2021

Sensors

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It is difficult to find correct correspondences for infrared and visible image registration because of different imaging principles. Traditional registration methods based on the point feature require designing the complicated feature descriptor and eliminate mismatched points, which results in unsatisfactory precision and much calculation time. To tackle these problems, this paper presents an artful method based on constrained point features to align infrared and visible images. The proposed method principally contains three steps. First, constrained point features are extracted by employing an object detection algorithm, which avoids constructing the complex feature descriptor and introduces the senior semantic information to improve the registration accuracy. Then, the left value rule (LV-rule) is designed to match constrained points strictly without the deletion of mismatched and redundant points. Finally, the affine transformation matrix is calculated according to matched point pairs. Moreover, this paper presents an evaluation method to automatically estimate registration accuracy. The proposed method is tested on a public dataset. Among all tested infrared-visible image pairs, registration results demonstrate that the proposed framework outperforms five state-of-the-art registration algorithms in terms of accuracy, speed, and robustness.

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“…An edge-preserving filtering and principal component analysis (PCA)-based visualization method is proposed in [29] for hyperspectral images. An enhanced affine transformation (EAT) was proposed in [30] for non-rigid IR and VIS image registration. A novel MRI reconstruction algorithm was developed in [31] with edge-preserving filter.…”

Section: Related Workmentioning

confidence: 99%

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

Natarajan¹,

Krishnan²

2022

Computer Systems Science and Engineering

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In existing methods for segmented images, either edge point extraction or preservation of edges, compromising contrast images is so sensitive to noise. The Degeneration Threshold Image Detection (DTID) framework has been proposed to improve the contrast of edge filtered images. Initially, DTID uses a Rapid Bilateral Filtering process for filtering edges of contrast images. This filter decomposes input images into base layers in the DTID framework. With minimal filtering time, Rapid Bilateral Filtering handles high dynamic contrast images for smoothening edge preservation. In the DTID framework, Rapid Bilateral Filtering with Shift-Invariant Base Pass Domain Filter is insensitive to noise. This Shift-Invariant Filtering estimates value across edges for removing outliers (i.e., noise preserving base layers of the contrast image). The intensity values are calculated in the base layer of the contrast image for accurately detecting nearby spatial locations using Shift-Invariant base Pass Domain Filter (SIDF). At last, Affine Planar Transformation is applied to detect edge filtered contrast images in the DTID framework for attaining a high quality of the image. It normalizes the translation and rotation of images. With this, Degeneration Threshold Image Detection maximizes average contrast enhancement quality and performs an experimental evaluation of factors such as detection accuracy, rate, and filtering time on contrast images. Experimental analysis shows that the DTID framework reduces the filtering time taken on contrast images by 54% and improves average contrast enhancement quality by 27% compared to GUMA, HMRF, SWT, and EHS. It provides better performance on the enhancement of average contrast enhancement quality by 28%, detection accuracy rate by 26%, and reduction in filtering time taken on contrast images by 30% compared to state-of-art methods.

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Non-rigid infrared and visible image registration by enhanced affine transformation

Cited by 27 publications

References 31 publications

Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

Multimodal Remote Sensing Image Registration Methods and Advancements: A Survey

An Infrared-Visible Image Registration Method Based on the Constrained Point Feature

Contrast Enhancement Based Image Detection Using Edge Preserved Key Pixel Point Filtering

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